AI-human collaboration will unleash innovation

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Margaret A. Boden, professor of Cognitive Science at Sussex University and author of the book, The Creative Mind: Myths and Mechanisms, suggested three criteria to assess if an idea or artefact is the product of creativity: it should be ‘New, Surprising and Valuable’. Christie’s recent auctioning of an AI artwork titled Portrait of Edmond de Belamy for $432,500 offers clear evidence that AI creativity has many takers. But the big question is whether AI processes can churn out truly surprising new ideas.

Ever since 1908, when French mathematician and philosopher Henri Poincare came up with his four-stage framework of developing a creative idea, it has remained the benchmark of an ideal creative process. The first stage of the creative process involves feeding the brain with all the information it will need. The second stage is trying to find connections and contradictions between all the collected information. The third stage is where the problem solver is expected to ‘walk out of the problem’ and engage in other activities that are relaxing. It is during this stage of relaxation that a creative idea is expected to leap out from nowhere. This is followed by the fourth stage, where the rough edges of the creative idea are chiselled to make it perfect.

There is no doubt that with the humongous level of information available on the world wide web, computers will be able to gather much more information than what a human brain can ever collect. With its huge computing power, today’s computers are far more efficient than the human brain in identifying links between various pieces of information. It is possible for computers to take care of the evaluation of a creative idea and hone it further to perfection. The question is whether digital machines can effectively perform the third stage of ‘walking out of the problem’. Many experts in creativity would argue that what happens in the third stage is what most determines whether the output will be an ordinary idea or a brilliantly creative one. At this stage of the creative process, one is moving the problem to the non-conscious level and using its tremendous efficiency to generate never-before connections in the human brain. These new connections are totally unexpected until they happen. This is what makes a truly creative idea so surprising. The exultation experienced upon such a discovery even has a legendary term, “eureka”, as Archimedes is said to have exclaimed in disregard of his state of undress. But AI can actually ‘walk out of a problem’ as humans can? Can AI replicate the smart non-conscious processes of the human brain?

A team at DeepMind developed AlphaGo, a computer program that could play Go, considered the world’s most complex board game. AlphaGo defeated the best human Go player, Lee Se-dol, with ease. A year later, DeepMind released AlphaGo Zero, an advanced version. This one was taught only the basic rules of the game. The rest it learnt by playing against itself millions of times. In three days, it defeated AlphaGo, 100 to nil. Today, there are many who believe that the hidden layers of the software’s deep neural networks that explain its enormous self-learning ability is equivalent to the vast and efficient non-conscious processes of the human brain.

If so, AI should be able churn out creative ideas in droves. Where does computational creativity go from here? Can AI become a harbinger of world-changing innovations?

Innovation is essentially about taking newly created ideas and developing them into something useful and practical. But converting creative ideas to innovation is no easy task. In its nascent stage, it is very difficult to distinguish a good creative idea from many other ordinary ideas. It is much like identifying a future success among lots of newborns in a paediatric ward. The history of great ideas reminds us that many a time, even innovators themselves do not realize the potential of their ideas for several years. For 10 years after his famous voyage, the idea of evolution just remained in Charles Darwin’s note books. It was only after Alfred Russel Wallace, another biologist, arrived at the same conclusions as those of Darwin, did he really started believing in the actual power of his proposition.

In the book, Where Good Ideas Come from: The Natural History of Innovation, Steven Johnson reminds us that to build a world-transforming innovation, just a brilliant idea alone is not enough. Many other things in the environment have to fall in place. For example, the idea of a video-sharing platform like You Tube would not have taken off if millions did not get access to the internet via a reasonably fast dial-up connection. The idea would have been deemed ‘ahead of its time’ had it been tried out before conditions turned favourable.

AI should be able to generate many new and surprising ideas. But much human intervention will be required to facilitate an efficient transition of these creative ideas to true innovations. It’s clear that the future of computational innovation will be a lot about stronger AI-human collaborations.

Biju Dominic is chief evangelist, Fractal Analytics, and chairman, FinalMile Consulting.

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